The energy consumption in large-scale data centers is attracting more and more attention today with the increasing data center energy costs making the enhanced performance very expensive. This is becoming a bottleneck...The energy consumption in large-scale data centers is attracting more and more attention today with the increasing data center energy costs making the enhanced performance very expensive. This is becoming a bottleneck to further developments in terms of both scale and performance of cloud computing. Thus, the reduction of the energy consumption by data centers is becoming a key research topic in green IT and green computing. The web servers providing cloud service computing run at various speeds for different scenarios. By shifting among these states using speed scaling, the energy consumption is proportional to the workload, which is termed energy-proportionality. This study uses stochastic service decision nets to investigate energy-efficient speed scaling on web servers. This model combines stochastic Petri nets with Markov decision process models. This enables the model to dynamically optimize the speed scaling strategy and make performance evaluations. The model is graphical and intuitive enough to characterize complicated system behavior and decisions. The model is service-oriented using the typical service patterns to reduce the complex model to a simple model with a smaller state space. Performance and reward equivalent analyse substantially reduces the system behavior sub-net. The model gives the optimal strategy and evaluates performance and energy metrics more concisely.展开更多
在绿色低碳经济大力倡导、“碳达峰、碳中和”能源政策加速部署背景下,新能源消纳问题日益凸显。因此,亟需分析新能源消纳的关键制约因素并进行综合量化评估,为新能源消纳问题的改善及解决提供依据。从“源网荷”以及调峰4方面构建新能...在绿色低碳经济大力倡导、“碳达峰、碳中和”能源政策加速部署背景下,新能源消纳问题日益凸显。因此,亟需分析新能源消纳的关键制约因素并进行综合量化评估,为新能源消纳问题的改善及解决提供依据。从“源网荷”以及调峰4方面构建新能源消纳制约因素多层级评估指标体系,提出一种结合模糊隶属度与动态弯曲算法(dynamic time warping,DTW)的模糊化动态评估方法,以样本模糊化的方法增加序列元素间差异性以避免模式匹配的错误,从而更客观地评估出影响新能源消纳的不同层级制约因素。仿真结果表明,该模糊化动态评估方法不仅能全面准确地对新能源消纳制约因素进行综合评估,还能为电网新能源消纳问题的解决提供数据支撑。展开更多
基金supported by the National Key Basic Research and Development (973) Program (Nos. 2012CB315801, 2011CB302805, 2010CB328105,and 2009CB320504)the National Natural Science Foundation of China (Nos. 60932003, 61020106002, and 61161140320)the Intel Research Council with the title of "Security Vulnerability Analysis based on Cloud Platform with Intel IA Architecture"
文摘The energy consumption in large-scale data centers is attracting more and more attention today with the increasing data center energy costs making the enhanced performance very expensive. This is becoming a bottleneck to further developments in terms of both scale and performance of cloud computing. Thus, the reduction of the energy consumption by data centers is becoming a key research topic in green IT and green computing. The web servers providing cloud service computing run at various speeds for different scenarios. By shifting among these states using speed scaling, the energy consumption is proportional to the workload, which is termed energy-proportionality. This study uses stochastic service decision nets to investigate energy-efficient speed scaling on web servers. This model combines stochastic Petri nets with Markov decision process models. This enables the model to dynamically optimize the speed scaling strategy and make performance evaluations. The model is graphical and intuitive enough to characterize complicated system behavior and decisions. The model is service-oriented using the typical service patterns to reduce the complex model to a simple model with a smaller state space. Performance and reward equivalent analyse substantially reduces the system behavior sub-net. The model gives the optimal strategy and evaluates performance and energy metrics more concisely.
文摘在绿色低碳经济大力倡导、“碳达峰、碳中和”能源政策加速部署背景下,新能源消纳问题日益凸显。因此,亟需分析新能源消纳的关键制约因素并进行综合量化评估,为新能源消纳问题的改善及解决提供依据。从“源网荷”以及调峰4方面构建新能源消纳制约因素多层级评估指标体系,提出一种结合模糊隶属度与动态弯曲算法(dynamic time warping,DTW)的模糊化动态评估方法,以样本模糊化的方法增加序列元素间差异性以避免模式匹配的错误,从而更客观地评估出影响新能源消纳的不同层级制约因素。仿真结果表明,该模糊化动态评估方法不仅能全面准确地对新能源消纳制约因素进行综合评估,还能为电网新能源消纳问题的解决提供数据支撑。
